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Text attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
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Soo-Min Kim and Eduard H. Hovy. 2004 · 2004
Earlier work this paper cites.
Feature selection, l1 vs. l2 regularization, and rotational invariance
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2012 · 2012
Earlier work this paper cites.
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Bing Liu. 2012 · 2012
Earlier work this paper cites.
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Matthew D. Zeiler. 2012 · 2012
Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Pengcheng Zhu and Yujiu Yang. 2017 · 2017
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Reinald Kim Amplayo, Jihyeok Kim, Sua Sung, and Seung-won Hwang. 2018a · 2018
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Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017 · 2017
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Zi-Yi Dou. 2017 · 2017
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